Manufacturing firms face great pressure to reduce their production costs continuously. One of the main expenditure sources for these firms is maintenance costs which can reach 15-70% of production costs. In capital intensive industries, maintenance costs as a percentage of total value-added costs can be as high as 20-50% in mining, 15-25% for primary metals and 3-15% for processing and manufacturing industries. For this reason there has been an increasing interest in the area of maintenance management. In contrast with corrective maintenance, where actions are performed after system failure, and time-based preventive maintenance which sets a periodic interval to perform preventive maintenance regardless of the system's health, condition-based maintenance (CBM) is a program that recommends actions based on the information collected through condition monitoring. A CBM program, if properly established and effectively implemented, can significantly reduce maintenance costs by reducing the number of unnecessary scheduled preventive maintenance operations.
A CBM program consists of three key steps: data acquisition to obtain data relevant to system health, data processing to handle and analyze the data collected and maintenance decision-making, recommending efficient maintenance actions.
Data acquisition is the process of collecting and storing useful data from the targeted system. This step in a CBM program has improved dramatically over the years due to the availability of many types of sensors at affordable prices.
Many models, algorithms and techniques have recently been available for data processing and analysis. They depend mainly on the type of data collected, and whether they are value type data, such as oil analysis, waveform type data such as vibration data, or multidimensional type data such as X-ray images. The process of extracting useful information from raw signals is called feature extraction. Extracted features are then used for device fault diagnostics, which is also called pattern recognition and classification.
Statistical techniques such as multivariate analysis and principal component analysis are used to extract useful features from raw maintenance data and to detect whether a specific fault is present or not, based on the condition monitoring information. Some researchers (Stellman C. M., Ewing K. J., Bucholtz F., Aggarwal I. D., Monitoring the degradation of a synthetic lubricant oil using infrared absorption fluorescence emission and multivariate analysis: A feasibility study. Lubrication Engineering, 1999, 55, 42-52) used multivariate analysis to study the deterioration of lubricants in device. Others (Allgood G. O., Upadhyaya B. R., A model-based high frequency matched filter arcing diagnostic system on principal component analysis (PCA) clustering. Application and Science of Computational Intelligence III, 2000, 4055, 430-440) proposed a condition diagnostic system based on the application of the principal components analysis (PCA) technique. The main drawback of statistical techniques is the necessity of making certain assumptions regarding the posteriori class probabilities.
Support vector machine (SVM) is also used extensively in device fault diagnostics, as described in (Korbicz J., Koscielny J. M., Kowalczuk Z., Cholewa W., Fault Diagnosis Models, Artificial Intelligence Applications, Springer, Berlin, 2004. lication and Science of Computational Intelligence III, 2000, 4055, 430-440), (Poyhonen P., Jover H. Hyotyniemi, Signal processing of vibrations for condition monitoring of an induction motor. ISCCP First International Symposium on Control Communications and Signal Processing, 2004, 499-502) and (Guo M, Xie L, Wang S. Q., Zhang J. M., research on an integrated ICA-SVN based framework for fault diagnosis. Proceedings of the 2003 IEEE International Conference on Systems, Man and Cybernetics, 2003, 3, 2710-2715). This technique finds an optimal hyperplane that maximizes the margin between two classes via mathematical programming (Bishop. C. M., Pattern Recognition and Machine Learning, Springer, 2006; Duda R. O., Hart P. E., Stork D. G., Pattern Classification, second, edition, John Wiley and Sons, 2001). The accuracy of this technique depends on the quality of the boundary curve found.
Another known method for extracting features relates to popular artificial intelligence technique for device fault diagnosis also known as artificial neural networks technique (ANN). Feedforward neural network (FFNN) is the most widely used neural network structure in device fault diagnosis (Fan Y., Li C. J., Diagnostics rule extraction from trained feedforward neural networks. Mechanical Systems and Signal Processing, 2002, 16, 1073-1081). The limitations of this technique are the difficulty in determining the network structure, the number of nodes, and the difficulty of interpreting the classification process.